Text-to-Speech
Transformers
Safetensors
VibeVoice
qwen2
text-generation
4bit
bitsandbytes
nf4
4-bit precision
Instructions to use tantk/vibevoice-1.5b-bnb-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tantk/vibevoice-1.5b-bnb-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-speech", model="tantk/vibevoice-1.5b-bnb-4bit")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tantk/vibevoice-1.5b-bnb-4bit") model = AutoModelForCausalLM.from_pretrained("tantk/vibevoice-1.5b-bnb-4bit") - VibeVoice
How to use tantk/vibevoice-1.5b-bnb-4bit with VibeVoice:
import torch, soundfile as sf, librosa, numpy as np from vibevoice.processor.vibevoice_processor import VibeVoiceProcessor from vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference # Load voice sample (should be 24kHz mono) voice, sr = sf.read("path/to/voice_sample.wav") if voice.ndim > 1: voice = voice.mean(axis=1) if sr != 24000: voice = librosa.resample(voice, sr, 24000) processor = VibeVoiceProcessor.from_pretrained("tantk/vibevoice-1.5b-bnb-4bit") model = VibeVoiceForConditionalGenerationInference.from_pretrained( "tantk/vibevoice-1.5b-bnb-4bit", torch_dtype=torch.bfloat16 ).to("cuda").eval() model.set_ddpm_inference_steps(5) inputs = processor(text=["Speaker 0: Hello!\nSpeaker 1: Hi there!"], voice_samples=[[voice]], return_tensors="pt") audio = model.generate(**inputs, cfg_scale=1.3, tokenizer=processor.tokenizer).speech_outputs[0] sf.write("output.wav", audio.cpu().numpy().squeeze(), 24000) - Notebooks
- Google Colab
- Kaggle
VibeVoice-1.5B - NF4 Quantized
4-bit NF4 quantization of microsoft/VibeVoice-1.5B.
Strategy
Backbone extraction approach:
- Downloaded raw safetensors (bypassed from_pretrained)
- Separated Qwen2.5-1.5B backbone from audio heads
- Quantized backbone as standard Qwen2ForCausalLM with NF4 + double quant
- Packaged quantized backbone + BF16 audio heads
| Component | Method | Size |
|---|---|---|
| LLM backbone (Qwen2.5-1.5B) | NF4 + double quant | ~0.8-1.0 GB |
| Audio heads (tokenizers, diffusion, connectors) | BF16 | ~1.8 GB |
Source
Quantized from microsoft/VibeVoice-1.5B (MIT license).
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Model tree for tantk/vibevoice-1.5b-bnb-4bit
Base model
microsoft/VibeVoice-1.5B